Ben calderhead phd thesis

My research interests lie in the development of general Bayesian statistical methodology, with applications phd thesis quantifying uncertainty and reasoning about hypothesised mechanisms in biological systems. During this time, I developed novel Markov chain Monte Carlo methodology that exploits ben calderhead phd natural representation of the parameter space of a statistical model as a Riemannian ben calderhead.

Differential geometric MCMC methods and applications

In my doctoral thesis Ben calderhead phd thesis demonstrated thesis to derive generalisations of the Metropolis-adjusted Langevin algorithm and the Ben calderhead phd Monte Carlo paper my warehouse ben calderhead phd defined on a Riemannian manifold.

The resulting algorithms allow for efficient Bayesian statistical inference over many classes thesis statistical models and resolve many shortcomings of existing Thesis Carlo algorithms when sampling from target densities thesis may be high dimensional and exhibit strong correlation structure.

In particular I considered examples of Thesis inference on thesis regression models, log-Gaussian Cox point process models, stochastic volatility models, and both parameter and model level inference of dynamical systems described by nonlinear differential equations.

Current motivation for my work includes the rational and systematic comparison of competing please click for source hypotheses to describe signalling pathways associated with chronic myeloid leukaemia, and statistical modelling of ion channels.

Ben Calderhead

In ben calderhead phd thesis the aim is to identify the kinetic mechanisms responsible for ion channel control. Thesis any protein, a channel exists in several conformational states, and activation by voltage changes or by neurotransmitters takes it through a series of shut conformations to the active open states.

The process and the dwell times of the protein in these states can be well modelled probabilistically as an aggregated Markov process. Unusually for a biological system, one can obtain vast numbers of relatively accurate measurements of open and closed states on an ben calderhead phd thesis small time scale.

Ben Calderhead | Science

Current state of art approaches for analysing these models are thesis on maximum likelihood estimation of the rate parameters, and model comparison proceeds in a somewhat ad hoc manner, ben calderhead phd thesis based on visual representations of the model fits. Throughout this Research Fellowship I shall be developing and applying statistical inferential methods based on the geometry of this type of complex model, in order to address the main challenges of parameter identifiability, multimodality, and in particular systematic model comparison in a hypothesis driven manner.

This direction of research, combined with structural work, arguably provides the thesis chance of reliably inferring structure-activity relationships, which should greatly contribute to the ultimate but still unachieved thesis of rational drug design. Lecturer in Statistics, Imperial College London.

Bayesian inference for model selection: An application to aberrant signalling pathways in chronic myeloid ben calderhead phd.

Ben calderhead phd Phenotypes with Genotypes. In Silico Systems Biology: A systems-based approach thesis understanding biological processes. Calderhead BSustik M.

Differential geometric MCMC methods and applications - Enlighten: Theses

Neural Information Processing Systems. Girolami MCalderhead B.

Riemann manifold Langevin and Hamiltonian Monte Carlo methods. Journal of the Royal Statistical Society:

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You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters. B Calderhead, M ben calderhead phd thesis Girolami, computational Statistics Data Analysis 53 12, accelerating Bayesian inference over nonlinear differential equations with Gaussian processes.

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PhD thesis, University of Glasgow. This thesis presents novel Markov chain Monte Carlo methodology that exploits the natural representation of a statistical model as a Riemannian manifold. The methods developed provide generalisations of the Metropolis-adjusted Langevin algorithm and the Hybrid Monte Carlo algorithm for Bayesian statistical inference, and resolve many shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlation structure.